When should I use dbt Community instead of Towards Data Science?

Getting help and best practices directly from active dbt practitioners and core contributors. Discovering dbt packages, macros, and community-built project patterns. Staying informed on dbt product updates, roadmap announcements, and ecosystem news

When should I use Towards Data Science instead of dbt Community?

Reading practitioner-written tutorials on Python data engineering tools and techniques. Finding how-to articles on integrating specific tools in real data pipelines. Discovering new tools and approaches via a curated, high-volume data publication

What are the main weaknesses of dbt Community?

Heavily dbt-focused — not useful for broader data engineering topics outside the dbt ecosystem. Slack format makes searching historical discussions slow and frustrating. High message volume can make it hard to surface the most relevant threads

What are the main weaknesses of Towards Data Science?

Content quality varies widely between authors; editorial review is limited. Many articles are paywalled and require a Medium subscription for full access. Tutorials can be outdated or oversimplified; always verify against official documentation

dbt Community vs Towards Data Science: Key Differences for Python Data Engineering

Communities & Learning

dbt Community

Analytics Engineering Hub

★ 4.8

N/A — community platform

N/A — web platform

Towards Data Science

Medium Publication

★ 4.5

N/A — publication platform

N/A — web platform

Side-by-Side Comparison

dbt Community

Towards Data Science

dbt Community

Towards Data Science

Best For

✓Getting help and best practices directly from active dbt practitioners and core contributors
✓Discovering dbt packages, macros, and community-built project patterns
✓Staying informed on dbt product updates, roadmap announcements, and ecosystem news

✓Reading practitioner-written tutorials on Python data engineering tools and techniques
✓Finding how-to articles on integrating specific tools in real data pipelines
✓Discovering new tools and approaches via a curated, high-volume data publication

Best For

✓Getting help and best practices directly from active dbt practitioners and core contributors
✓Discovering dbt packages, macros, and community-built project patterns
✓Staying informed on dbt product updates, roadmap announcements, and ecosystem news

✓Reading practitioner-written tutorials on Python data engineering tools and techniques
✓Finding how-to articles on integrating specific tools in real data pipelines
✓Discovering new tools and approaches via a curated, high-volume data publication

Weaknesses

•Heavily dbt-focused — not useful for broader data engineering topics outside the dbt ecosystem
•Slack format makes searching historical discussions slow and frustrating
•High message volume can make it hard to surface the most relevant threads

•Content quality varies widely between authors; editorial review is limited
•Many articles are paywalled and require a Medium subscription for full access
•Tutorials can be outdated or oversimplified; always verify against official documentation

Weaknesses

•Heavily dbt-focused — not useful for broader data engineering topics outside the dbt ecosystem
•Slack format makes searching historical discussions slow and frustrating
•High message volume can make it hard to surface the most relevant threads

•Content quality varies widely between authors; editorial review is limited
•Many articles are paywalled and require a Medium subscription for full access
•Tutorials can be outdated or oversimplified; always verify against official documentation

License

N/A — community platform

N/A — publication platform

License

N/A — community platform

N/A — publication platform

Install

N/A — web platform

Install

N/A — web platform

Rating

★ 4.8

★ 4.5

Rating

★ 4.8

★ 4.5

Key Features

dbt Community

1Official dbt Slack with 50k+ members across dbt Labs and community channels
2dbt Discourse forum for technical Q&A and package announcements
3Monthly dbt meetups and the annual Coalesce conference
4Community-maintained dbt packages published to the dbt Hub registry
5Channels dedicated to specific platforms: Snowflake, BigQuery, Redshift, Spark

Towards Data Science

1Medium publication with 700k+ subscribers focused on data science and engineering
2Practitioner-written tutorials on Python libraries and pipeline patterns
3Case studies from engineers at Netflix, Airbnb, Uber, and other tech companies
4Covers MLOps, orchestration, streaming, and data quality in depth
5Searchable archive of thousands of Python data engineering articles

How Python Data Engineers Use These Tools

dbt Community

Python data engineers working with dbt use the community Slack to troubleshoot Jinja macro issues, discover community packages that extend dbt's functionality with Python, and stay current on dbt Python model best practices. The Discourse forum is the authoritative source for answers to advanced dbt questions not covered in the official docs.

Towards Data Science

Python data engineers read Towards Data Science to discover practical implementation patterns for new tools — step-by-step tutorials on integrating dbt with Airflow, building streaming pipelines with Kafka and Python, or implementing data quality checks with Great Expectations. Articles bridge the gap between documentation and real-world usage.

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Individual Tool Pages

View dbt Community details →View Towards Data Science details →

Side-by-Side Comparison

dbt Community

Towards Data Science

dbt Community

Towards Data Science

Best For

✓Getting help and best practices directly from active dbt practitioners and core contributors
✓Discovering dbt packages, macros, and community-built project patterns
✓Staying informed on dbt product updates, roadmap announcements, and ecosystem news

✓Reading practitioner-written tutorials on Python data engineering tools and techniques
✓Finding how-to articles on integrating specific tools in real data pipelines
✓Discovering new tools and approaches via a curated, high-volume data publication

Best For

✓Getting help and best practices directly from active dbt practitioners and core contributors
✓Discovering dbt packages, macros, and community-built project patterns
✓Staying informed on dbt product updates, roadmap announcements, and ecosystem news

✓Reading practitioner-written tutorials on Python data engineering tools and techniques
✓Finding how-to articles on integrating specific tools in real data pipelines
✓Discovering new tools and approaches via a curated, high-volume data publication

Weaknesses

•Heavily dbt-focused — not useful for broader data engineering topics outside the dbt ecosystem
•Slack format makes searching historical discussions slow and frustrating
•High message volume can make it hard to surface the most relevant threads

•Content quality varies widely between authors; editorial review is limited
•Many articles are paywalled and require a Medium subscription for full access
•Tutorials can be outdated or oversimplified; always verify against official documentation

Weaknesses

•Heavily dbt-focused — not useful for broader data engineering topics outside the dbt ecosystem
•Slack format makes searching historical discussions slow and frustrating
•High message volume can make it hard to surface the most relevant threads

•Content quality varies widely between authors; editorial review is limited
•Many articles are paywalled and require a Medium subscription for full access
•Tutorials can be outdated or oversimplified; always verify against official documentation

License

N/A — community platform

N/A — publication platform

License

N/A — community platform

N/A — publication platform

Install

N/A — web platform

Install

N/A — web platform

Rating

★ 4.8

★ 4.5

Rating

★ 4.8

★ 4.5

Key Features

dbt Community

1Official dbt Slack with 50k+ members across dbt Labs and community channels
2dbt Discourse forum for technical Q&A and package announcements
3Monthly dbt meetups and the annual Coalesce conference
4Community-maintained dbt packages published to the dbt Hub registry
5Channels dedicated to specific platforms: Snowflake, BigQuery, Redshift, Spark

Towards Data Science

1Medium publication with 700k+ subscribers focused on data science and engineering
2Practitioner-written tutorials on Python libraries and pipeline patterns
3Case studies from engineers at Netflix, Airbnb, Uber, and other tech companies
4Covers MLOps, orchestration, streaming, and data quality in depth
5Searchable archive of thousands of Python data engineering articles

How Python Data Engineers Use These Tools